Detecting Hostile Posts using Relational Graph Convolutional Network
This work addresses the problem of identifying hostile content in Hindi social media posts, which is important for content moderation and platform safety.
This paper describes a model for detecting and classifying hostile posts in Hindi on social media, categorizing them into fake, offensive, hate, and defamation. The proposed Relational Graph Convolutional Network (RGCN) achieved an F1 score of 0.97 on coarse-grained evaluation, ranking 7th, and secured the best performance in identifying fake posts.
This work is based on the submission to the competition Hindi Constraint conducted by AAAI@2021 for detection of hostile posts in Hindi on social media platforms. Here, a model is presented for detection and classification of hostile posts and further classify into fake, offensive, hate and defamation using Relational Graph Convolutional Networks. Unlike other existing work, our approach is focused on using semantic meaning along with contextutal information for better classification. The results from AAAI@2021 indicates that the proposed model is performing at par with Google's XLM-RoBERTa on the given dataset. Our best submission with RGCN achieves an F1 score of 0.97 (7th Rank) on coarse-grained evaluation and achieved best performance on identifying fake posts. Among all submissions to the challenge, our classification system with XLM-Roberta secured 2nd rank on fine-grained classification.